class_docstr: Pointwise exponential of a positive semi-definite kernel.
  
  Produces `exp(k(x, y))`, where `k(., .)` is the input positive semi-definite
  kernel.
  
  #### References
  [1]: John Shawe-Taylor and Nello Cristianini. Kernel Methods for Pattern
  Analysis. Section 3.2. 2004.
  https://people.eecs.berkeley.edu/~jordan/kernels/0521813972c03_p47-84.pdf
docstr: Construct pointwise exponential of a input kernel.
  
  Args:
    kernel: Tensorflow Probability PSD kernel instance.
    validate_args: If `True`, parameters are checked for validity despite
      possibly degrading runtime performance
    name: Python `str` name prefixed to Ops created by this class.
docstr_tok: ["Construct","pointwise","exponential","of","a","input","kernel",".","Args",":","kernel",":","Tensorflow","Probability","PSD","kernel","instance",".","validate_args",":","If","`","True","`",",","parameters","are","checked","for","validity","despite","possibly","degrading","runtime","performance","name",":","Python","`","str","`","name","prefixed","to","Ops","created","by","this","class","."]
code: def __init__(self, kernel, validate_args=False, name="Exponential"):
      """Construct pointwise exponential of a input kernel.
  
      Args:
        kernel: Tensorflow Probability PSD kernel instance.
        validate_args: If `True`, parameters are checked for validity despite
          possibly degrading runtime performance
        name: Python `str` name prefixed to Ops created by this class.
      """
      parameters = dict(locals())
  
      with tf.name_scope(name) as name:
          self._kernel = kernel
          super(PointwiseExponential, self).__init__(
              feature_ndims=kernel.feature_ndims,
              dtype=kernel.dtype,
              validate_args=validate_args,
              name=name,
              parameters=parameters,
          )
  
file_path: probability/tensorflow_probability/python/math/psd_kernels/pointwise_exponential.py
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.math.psd_kernels import positive_semidefinite_kernel
label_desc: Use image node_id 1 to create a new PointwiseExponential object from inherited base classes: psd_kernel with example: obj = PointwiseExponential(kernel, validate_args, name)